{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 便利店销量预测\n",
    "\n",
    "这是[便利店销量预测比赛](https://www.kaggle.com/c/rossmann-store-sales)的一个使用 XGBoost 的简单尝试参考。\n",
    "\n",
    "得分 ： \n",
    "\n",
    "Private Score \n",
    "0.12449\n",
    "\n",
    "Public Score\n",
    "0.11235"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-06T11:51:45.753291Z",
     "start_time": "2019-10-06T11:51:45.746309Z"
    }
   },
   "source": [
    "<h1 class=\"page-name\">\n",
    "    Forecast sales using store, promotion, and competitor data\n",
    "</h1>\n",
    "\n",
    "\n",
    "<p>Rossmann operates over 3,000 drug stores in 7 European countries. Currently, <br />Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied.</p>\n",
    "<p><span style=\"font-size: 1em; line-height: 1.5em;\">In their first Kaggle competition, Rossmann is challenging you to predict 6 weeks of daily sales for 1,115 stores located across Germany. Reliable sales forecasts enable store managers to create effective staff schedules that increase productivity and motivation. By helping Rossmann create a robust prediction model, you will help store managers stay focused on what’s most important to them: their customers and their teams! </span></p>\n",
    "<p><span style=\"font-size: 1em; line-height: 1.5em;\"> <img src=\"https://kaggle2.blob.core.windows.net/competitions/kaggle/4594/media/rossmann_banner2.png\" alt=\"\" height=\"81\" width=\"640\" /><br /></span></p>\n",
    "<p><em><span style=\"font-size: 1em; line-height: 1.5em;\">If you are interested in joining Rossmann at their headquarters near Hanover, Germany, please contact Mr. Frank König (Frank.Koenig {at} rossmann.de) Rossmann is currently recruiting data scientists at <a href=\"http://www.rossmann.de/unternehmen/karriere/stellenboerse/stellenanzeigen~jid=3A5205E3-C4F9-4F5D-AA93-438D0B064D70~\">senior</a> and <a href=\"http://www.rossmann.de/unternehmen/karriere/stellenboerse/stellenanzeigen~jid=F5142F37-C823-4767-B7CF-21DE3B351D66~\">entry-level</a> positions.</span></em></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据\n",
    "\n",
    "<table id=\"data-files\" class=\"nicetable full roomy align-top border\">   \n",
    "<thead>\n",
    "    <tr>\n",
    "        <th colspan=\"2\">File Name</th> \n",
    "        <th>Available Formats</th>         \n",
    "    </tr> \n",
    "</thead>\n",
    "\n",
    "    <tbody  >\n",
    "        <tr>\n",
    "\n",
    "            <td class=\"file-name\" colspan=\"2\" rowspan=\"1\">sample_submission.csv</td>\n",
    "            <td>\n",
    "<a href=\"/c/rossmann-store-sales/download/sample_submission.csv.zip\" name=\"sample_submission.csv.zip\" onclick=\"window.Intercom(&#39;trackEvent&#39;,&#39;download_compdata&#39;,{&#39;comp_id&#39;: 4594});\">.zip (55.25 kb)</a>                    </td>\n",
    "        </tr>\n",
    "\n",
    "    </tbody>\n",
    "    <tbody  >\n",
    "        <tr>\n",
    "\n",
    "            <td class=\"file-name\" colspan=\"2\" rowspan=\"1\">store.csv</td>\n",
    "            <td>\n",
    "<a href=\"/c/rossmann-store-sales/download/store.csv.zip\" name=\"store.csv.zip\" onclick=\"window.Intercom(&#39;trackEvent&#39;,&#39;download_compdata&#39;,{&#39;comp_id&#39;: 4594});\">.zip (8.33 kb)</a>                    </td>\n",
    "        </tr>\n",
    "\n",
    "    </tbody>\n",
    "    <tbody  >\n",
    "        <tr>\n",
    "\n",
    "            <td class=\"file-name\" colspan=\"2\" rowspan=\"1\">test.csv</td>\n",
    "            <td>\n",
    "<a href=\"/c/rossmann-store-sales/download/test.csv.zip\" name=\"test.csv.zip\" onclick=\"window.Intercom(&#39;trackEvent&#39;,&#39;download_compdata&#39;,{&#39;comp_id&#39;: 4594});\">.zip (143.25 kb)</a>                    </td>\n",
    "        </tr>\n",
    "\n",
    "    </tbody>\n",
    "    <tbody  >\n",
    "        <tr>\n",
    "\n",
    "            <td class=\"file-name\" colspan=\"2\" rowspan=\"1\">train.csv</td>\n",
    "            <td>\n",
    "<a href=\"/c/rossmann-store-sales/download/train.csv.zip\" name=\"train.csv.zip\" onclick=\"window.Intercom(&#39;trackEvent&#39;,&#39;download_compdata&#39;,{&#39;comp_id&#39;: 4594});\">.zip (5.66 mb)</a>                    </td>\n",
    "        </tr>\n",
    "\n",
    "    </tbody>\n",
    "</table>\n",
    "\n",
    "<p>You are provided with historical sales data for 1,115 Rossmann stores. The task is to forecast the \"Sales\" column for the test set. Note that some stores in the dataset were temporarily closed for refurbishment.</p>\n",
    "\n",
    "###  Files\n",
    "<ul>\n",
    "<li><strong>train.csv</strong> - historical data including Sales</li>\n",
    "<li><strong>test.csv</strong> - historical data excluding Sales</li>\n",
    "<li><strong>sample_submission.csv</strong> - a sample submission file in the correct format</li>\n",
    "<li><strong>store.csv</strong> - supplemental information about the stores</li>\n",
    "</ul>\n",
    "\n",
    "### Data fields\n",
    "\n",
    "<p>Most of the fields are self-explanatory. The following are descriptions for those that aren't.</p>\n",
    "<ul>\n",
    "<li><strong>Id</strong> - an Id that represents a (Store, Date) duple within the test set</li>\n",
    "<li><strong>Store</strong> - a unique Id for each store</li>\n",
    "<li><strong>Sales</strong> - the turnover for any given day (this is what you are predicting)</li>\n",
    "<li><strong>Customers</strong> - the number of customers on a given day</li>\n",
    "<li><strong>Open</strong> - an indicator for whether the store was open: 0 = closed, 1 = open</li>\n",
    "<li><strong>StateHoliday</strong> - indicates a state holiday. Normally all stores, with few exceptions, are closed on state holidays. Note that all schools are closed on public holidays and weekends. a = public holiday, b = Easter holiday, c = Christmas, 0 = None</li>\n",
    "<li><strong>SchoolHoliday</strong> - indicates if the (Store, Date) was affected by the closure of public schools</li>\n",
    "<li><strong>StoreType</strong> - differentiates between 4 different store models: a, b, c, d</li>\n",
    "<li><strong>Assortment</strong> - describes an assortment level: a = basic, b = extra, c = extended</li>\n",
    "<li><strong>CompetitionDistance</strong> - distance in meters to the nearest competitor store</li>\n",
    "<li><strong>CompetitionOpenSince[Month/Year]</strong> - gives the approximate year and month of the time the nearest competitor was opened</li>\n",
    "<li><strong>Promo</strong> - indicates whether a store is running a promo on that day</li>\n",
    "<li><strong>Promo2</strong> - Promo2 is a continuing and consecutive promotion for some stores: 0 = store is not participating, 1 = store is participating</li>\n",
    "<li><strong>Promo2Since[Year/Week]</strong> - describes the year and calendar week when the store started participating in Promo2</li>\n",
    "<li><strong>PromoInterval</strong> - describes the consecutive intervals Promo2 is started, naming the months the promotion is started anew. E.g. \"Feb,May,Aug,Nov\" means each round starts in February, May, August, November of any given year for that store</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 引入所需的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-11T13:14:41.429120Z",
     "start_time": "2019-10-11T13:14:40.948890Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime\n",
    "import csv\n",
    "import numpy as np\n",
    "import os\n",
    "import scipy as sp\n",
    "import xgboost as xgb\n",
    "import itertools\n",
    "import operator\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-11T12:57:01.642929Z",
     "start_time": "2019-10-11T12:57:01.453361Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.base import TransformerMixin\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib import pylab as plt\n",
    "plot = True\n",
    "\n",
    "goal = 'Sales'\n",
    "myid = 'Id'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义一些变换和判准则\n",
    "\n",
    "https://www.kaggle.com/c/rossmann-store-sales/overview/evaluation\n",
    "\n",
    "由于赛题的评估函数是Root Mean Square Percentage Error (RMSPE)，这个评估函数会对一些特别高的销量数据的实际值和预测值的代价进行一定的优化。比如销量 1500 预测 1495 ，销量15 预测 10，这两个情况是不一样的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-11T13:41:44.647633Z",
     "start_time": "2019-10-11T13:41:44.640679Z"
    }
   },
   "outputs": [],
   "source": [
    "def ToWeight(y):\n",
    "    w = np.zeros(y.shape, dtype=float)\n",
    "    ind = y !=0\n",
    "    # 非0数据的平方的倒数\n",
    "    w[ind] =  1./(y[ind]**2)\n",
    "    return w\n",
    "\n",
    "def rmspe(yhat, y):\n",
    "    \"\"\"\n",
    "    评估函数\n",
    "    \"\"\"\n",
    "    w = ToWeight(y)\n",
    "    rmspe = np.sqrt(np.mean(w * (y - yhat)**2))\n",
    "    return rmspe\n",
    "\n",
    "\n",
    "def rmspe_xg(yhat, y):\n",
    "    \"\"\"\n",
    "    自定义 xgboost 评估函数，只在评估时使用，训练时不适使用。\n",
    "    需要训练时使用的话，要提供一阶导数和二阶导数，这里不方便\n",
    "    \n",
    "    这里评估的时候用了 np.exp 是因为我们在后面训练的时候打算对销量做 log 变化，防止一些销量特别高的数据的影响\n",
    "    \"\"\"\n",
    "    y = y.get_label()\n",
    "    y = np.exp(y) - 1\n",
    "    yhat = np.exp(yhat) -1\n",
    "    w = ToWeight(y)\n",
    "    rmspe = np.sqrt(np.mean(w * (y - yhat)**2))\n",
    "    return \"rmspe\", rmspe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-10T14:28:54.021539Z",
     "start_time": "2019-10-10T14:28:53.853822Z"
    }
   },
   "outputs": [],
   "source": [
    "store = pd.read_csv('../../data/rossmann_store_sales/store.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-10T14:28:59.758523Z",
     "start_time": "2019-10-10T14:28:59.742557Z"
    }
   },
   "outputs": [
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       "      <th>CompetitionDistance</th>\n",
       "      <th>CompetitionOpenSinceMonth</th>\n",
       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <th>Promo2</th>\n",
       "      <th>Promo2SinceWeek</th>\n",
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       "      <td>9.0</td>\n",
       "      <td>2009.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "   Store StoreType Assortment  CompetitionDistance  CompetitionOpenSinceMonth  \\\n",
       "0      1         c          a               1270.0                        9.0   \n",
       "1      2         a          a                570.0                       11.0   \n",
       "2      3         a          a              14130.0                       12.0   \n",
       "3      4         c          c                620.0                        9.0   \n",
       "4      5         a          a              29910.0                        4.0   \n",
       "\n",
       "   CompetitionOpenSinceYear  Promo2  Promo2SinceWeek  Promo2SinceYear  \\\n",
       "0                    2008.0       0              NaN              NaN   \n",
       "1                    2007.0       1             13.0           2010.0   \n",
       "2                    2006.0       1             14.0           2011.0   \n",
       "3                    2009.0       0              NaN              NaN   \n",
       "4                    2015.0       0              NaN              NaN   \n",
       "\n",
       "     PromoInterval  \n",
       "0              NaN  \n",
       "1  Jan,Apr,Jul,Oct  \n",
       "2  Jan,Apr,Jul,Oct  \n",
       "3              NaN  \n",
       "4              NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-10T14:29:32.167338Z",
     "start_time": "2019-10-10T14:29:30.403190Z"
    }
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('../../data/rossmann_store_sales/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-10T14:29:34.972395Z",
     "start_time": "2019-10-10T14:29:34.963419Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>4822</td>\n",
       "      <td>559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  DayOfWeek        Date  Sales  Customers  Open  Promo StateHoliday  \\\n",
       "0      1          5  2015-07-31   5263        555     1      1            0   \n",
       "1      2          5  2015-07-31   6064        625     1      1            0   \n",
       "2      3          5  2015-07-31   8314        821     1      1            0   \n",
       "3      4          5  2015-07-31  13995       1498     1      1            0   \n",
       "4      5          5  2015-07-31   4822        559     1      1            0   \n",
       "\n",
       "   SchoolHoliday  \n",
       "0              1  \n",
       "1              1  \n",
       "2              1  \n",
       "3              1  \n",
       "4              1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-10T14:30:02.706278Z",
     "start_time": "2019-10-10T14:30:02.633521Z"
    }
   },
   "outputs": [],
   "source": [
    "test_df = pd.read_csv('../../data/rossmann_store_sales/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-10T14:30:06.481013Z",
     "start_time": "2019-10-10T14:30:06.473034Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-09-17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-09-17</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>2015-09-17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-09-17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-09-17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Store  DayOfWeek        Date  Open  Promo StateHoliday  SchoolHoliday\n",
       "0   1      1          4  2015-09-17   1.0      1            0              0\n",
       "1   2      3          4  2015-09-17   1.0      1            0              0\n",
       "2   3      7          4  2015-09-17   1.0      1            0              0\n",
       "3   4      8          4  2015-09-17   1.0      1            0              0\n",
       "4   5      9          4  2015-09-17   1.0      1            0              0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-11T13:14:15.282415Z",
     "start_time": "2019-10-11T13:14:15.276432Z"
    }
   },
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    \"\"\"\n",
    "    加载数据，设定数值型和非数值型数据\n",
    "    \"\"\"\n",
    "    store = pd.read_csv('../../data/rossmann_store_sales/store.csv')\n",
    "    train_org = pd.read_csv('../../data/rossmann_store_sales/train.csv',dtype={'StateHoliday':pd.np.string_})\n",
    "    test_org = pd.read_csv('../../data/rossmann_store_sales/test.csv',dtype={'StateHoliday':pd.np.string_})\n",
    "    train = pd.merge(train_org, store, on='Store', how='left') \n",
    "    test = pd.merge(test_org, store, on='Store', how='left') \n",
    "    features = test.columns.tolist()\n",
    "    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "    features_numeric = test.select_dtypes(include=numerics).columns.tolist()\n",
    "    features_none_numeric = [f for f in features if f not in features_numeric]\n",
    "    return (train, test, features, features_none_numeric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据与特征处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-11T13:41:49.494692Z",
     "start_time": "2019-10-11T13:41:49.472728Z"
    }
   },
   "outputs": [],
   "source": [
    "def process_data(train, test, features, features_non_numeric):\n",
    "    \"\"\"\n",
    "    特征工程与特征选择\n",
    "    \"\"\"\n",
    "    train = train[train['Sales'] > 0]\n",
    "    \n",
    "    for data in [train, test]:\n",
    "        # 时间特征 这里可以直接用 to_datetime 方便处理\n",
    "        data['year'] = data.Date.apply(lambda x: x.split('-')[0])\n",
    "        data['year'] = data['year'].astype(float)\n",
    "        data['month'] = data.Date.apply(lambda x: x.split('-')[1])\n",
    "        data['month'] = data['month'].astype(float)\n",
    "        data['day'] = data.Date.apply(lambda x: x.split('-')[2])\n",
    "        data['day'] = data['day'].astype(float)\n",
    "        \n",
    "        # 特定的促销月份\n",
    "        data['promojan'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if \"Jan\" in x else 0)\n",
    "        data['promofeb'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if \"Feb\" in x else 0)\n",
    "        data['promomar'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if \"Mar\" in x else 0)\n",
    "        data['promoapr'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if \"Apr\" in x else 0)\n",
    "        data['promomay'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if \"May\" in x else 0)\n",
    "        data['promojun'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if \"Jun\" in x else 0)\n",
    "        data['promojul'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if \"Jul\" in x else 0)\n",
    "        data['promoaug'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if \"Aug\" in x else 0)\n",
    "        data['promosep'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if \"Sep\" in x else 0)\n",
    "        data['promooct'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if \"Oct\" in x else 0)\n",
    "        data['promonov'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if \"Nov\" in x else 0)\n",
    "        data['promodec'] = data.PromoInterval.apply(lambda x: 0 if isinstance(x, float) else 1 if \"Dec\" in x else 0)\n",
    "        \n",
    "        \n",
    "    # 特征集合\n",
    "    noisy_features = [myid, 'Date']\n",
    "    features = [c for c in features if c not in noisy_features]\n",
    "    features_non_numeric = [c for c in features_non_numeric if c not in noisy_features]\n",
    "    features.extend(['year', 'month', 'day'])\n",
    "    \n",
    "    # 缺失值填充\n",
    "    # 继承TransformerMixin\n",
    "    class DataFrameImputer(TransformerMixin):\n",
    "        # http://stackoverflow.com/questions/25239958/impute-categorical-missing-values-in-scikit-learn\n",
    "        def __init__(self):\n",
    "            \"\"\"Impute missing values.\n",
    "            Columns of dtype object are imputed with the most frequent value\n",
    "            in column.\n",
    "            Columns of other types are imputed with mean of column.\n",
    "            \"\"\"\n",
    "        def fit(self, X, y=None):\n",
    "            self.fill = pd.Series([X[c].value_counts().index[0] # mode\n",
    "                if X[c].dtype == np.dtype('O') else X[c].mean() for c in X], # mean\n",
    "                index=X.columns)\n",
    "            return self\n",
    "        def transform(self, X, y=None):\n",
    "            return X.fillna(self.fill)\n",
    "        \n",
    "    train = DataFrameImputer().fit_transform(train)\n",
    "    test = DataFrameImputer().fit_transform(test)\n",
    "    \n",
    "    # 预处理非数值型特征\n",
    "    le = LabelEncoder()\n",
    "    for col in features_non_numeric:\n",
    "        # 训练集和测试集一起处理\n",
    "        le.fit(list(train[col])+list(test[col]))\n",
    "        train[col] = le.transform(train[col])\n",
    "        test[col] = le.transform(test[col])\n",
    "    # LR和神经网络这种模型都对输入数据的幅度极度敏感，请先做归一化操作\n",
    "    scaler = StandardScaler()\n",
    "    for col in set(features) - set(features_non_numeric) - \\\n",
    "      set([]): # TODO: add what not to scale\n",
    "        scaler.fit(np.array(list(train[col])+list(test[col])).reshape(-1,1))\n",
    "        train[col] = scaler.transform(np.array(train[col]).reshape(-1,1))\n",
    "        test[col] = scaler.transform(np.array(test[col]).reshape(-1,1))\n",
    "    return (train,test, features, features_non_numeric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练与分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-12T07:43:56.938675Z",
     "start_time": "2019-10-12T07:43:56.924711Z"
    }
   },
   "outputs": [],
   "source": [
    "def XGB_native(train, test, features, features_non_numeric):\n",
    "    depth = 13\n",
    "    eta = 0.01 \n",
    "    ntrees = 5000\n",
    "    mcw = 3\n",
    "    parms = {\"objective\": \"reg:linear\",\n",
    "            \"booster\": \"gbtree\",\n",
    "            \"eta\": eta,\n",
    "            \"max_depth\": depth,\n",
    "            \"min_child_weigh\": mcw,\n",
    "            \"subsample\": 0.9,\n",
    "            \"colsample_bytree\": 0.7,\n",
    "            \"silent\": 1}\n",
    "    print(\"Running with parms:\" + str(parms))\n",
    "    print(\"Running with ntrees: \" + str(ntrees))\n",
    "    print(\"Running with features: \" + str(features))\n",
    "    \n",
    "    # 训练模型\n",
    "    tsize = 0.05\n",
    "    X_train, X_test = train_test_split(train, test_size=tsize)\n",
    "    # log 变换后进行训练，数据特别大的情况\n",
    "    dtrain = xgb.DMatrix(X_train[features], np.log(X_train[goal] + 1))\n",
    "    dvalid = xgb.DMatrix(X_test[features], np.log(X_test[goal] + 1))\n",
    "    watchlist = [(dvalid, 'eval'), (dtrain, 'train')]\n",
    "    gbm = xgb.train(parms, dtrain, ntrees, evals=watchlist, early_stopping_rounds=100,feval=rmspe_xg, verbose_eval=True)\n",
    "    # 预测结果\n",
    "    train_probs = gbm.predict(xgb.DMatrix(X_test[features]))\n",
    "    # 销量不为0 以下\n",
    "    indices = train_probs < 0\n",
    "    train_probs[indices] = 0\n",
    "    error = rmspe(np.exp(train_probs) - 1, X_test[goal].values)\n",
    "    print(error)\n",
    "    \n",
    "    # 预测和输出\n",
    "    test_probs = gbm.predict(xgb.DMatrix(test[features]))\n",
    "    indices = test_probs < 0\n",
    "    test_probs[indices] = 0\n",
    "    submission = pd.DataFrame({myid: test[myid], goal: np.exp(test_probs) -1})\n",
    "    if not os.path.exists('result/'):\n",
    "        os.mkdir('result/')\n",
    "    submission.to_csv(\"./result/dat-xgb_d%s_eta%s_ntree%s_mcw%s_tsize%s.csv\" % (str(depth),str(eta),str(ntrees),str(mcw),str(tsize)) , index=False)\n",
    "    \n",
    "    # 特征重要性\n",
    "    if plot:\n",
    "        outfile = open('xgb.fmap', 'w')\n",
    "        i = 0\n",
    "        for feat in features:\n",
    "          outfile.write('{0}\\t{1}\\tq\\n'.format(i, feat))\n",
    "          i = i + 1\n",
    "        outfile.close()\n",
    "        importance = gbm.get_fscore(fmap='xgb.fmap')\n",
    "        importance = sorted(importance.items(), key=operator.itemgetter(1))\n",
    "        df = pd.DataFrame(importance, columns=['feature', 'fscore'])\n",
    "        df['fscore'] = df['fscore'] / df['fscore'].sum()\n",
    "        # Plotitup\n",
    "        plt.figure()\n",
    "        df.plot()\n",
    "        df.plot(kind='barh', x='feature', y='fscore', legend=False, figsize=(25, 15))\n",
    "        plt.title('XGBoost Feature Importance')\n",
    "        plt.xlabel('relative importance')\n",
    "        plt.gcf().savefig('Feature_Importance_xgb_d%s_eta%s_ntree%s_mcw%s_tsize%s.png' % (str(depth),str(eta),str(ntrees),str(mcw),str(tsize)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-11T13:41:51.546303Z",
     "start_time": "2019-10-11T13:41:50.639519Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=> 载入数据中...\n"
     ]
    }
   ],
   "source": [
    "print(\"=> 载入数据中...\")\n",
    "train,test,features,features_non_numeric = load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-11T13:42:02.947235Z",
     "start_time": "2019-10-11T13:41:51.624576Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=> 处理数据与特征工程...\n"
     ]
    }
   ],
   "source": [
    "print(\"=> 处理数据与特征工程...\")\n",
    "train,test,features,features_non_numeric = process_data(train,test,features,features_non_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-12T08:20:29.386490Z",
     "start_time": "2019-10-12T07:44:02.970340Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=> 使用XGBoost建模...\n",
      "Running with parms:{'objective': 'reg:linear', 'booster': 'gbtree', 'eta': 0.01, 'max_depth': 13, 'min_child_weigh': 3, 'subsample': 0.9, 'colsample_bytree': 0.7, 'silent': 1}\n",
      "Running with ntrees: 5000\n",
      "Running with features: ['Store', 'DayOfWeek', 'Open', 'Promo', 'StateHoliday', 'SchoolHoliday', 'StoreType', 'Assortment', 'CompetitionDistance', 'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2', 'Promo2SinceWeek', 'Promo2SinceYear', 'PromoInterval', 'year', 'month', 'day']\n",
      "[0]\teval-rmse:8.18483\ttrain-rmse:8.18619\teval-rmspe:0.999863\ttrain-rmspe:0.999864\n",
      "Multiple eval metrics have been passed: 'train-rmspe' will be used for early stopping.\n",
      "\n",
      "Will train until train-rmspe hasn't improved in 100 rounds.\n",
      "[1]\teval-rmse:8.10313\ttrain-rmse:8.1045\teval-rmspe:0.999837\ttrain-rmspe:0.999838\n",
      "[2]\teval-rmse:8.02226\ttrain-rmse:8.02363\teval-rmspe:0.999809\ttrain-rmspe:0.99981\n",
      "[3]\teval-rmse:7.94218\ttrain-rmse:7.94353\teval-rmspe:0.999779\ttrain-rmspe:0.99978\n",
      "[4]\teval-rmse:7.8629\ttrain-rmse:7.86425\teval-rmspe:0.999747\ttrain-rmspe:0.999748\n",
      "[5]\teval-rmse:7.78445\ttrain-rmse:7.78578\teval-rmspe:0.999713\ttrain-rmspe:0.999713\n",
      "[6]\teval-rmse:7.70675\ttrain-rmse:7.70806\teval-rmspe:0.999676\ttrain-rmspe:0.999676\n",
      "[7]\teval-rmse:7.62984\ttrain-rmse:7.63114\teval-rmspe:0.999636\ttrain-rmspe:0.999637\n",
      "[8]\teval-rmse:7.55368\ttrain-rmse:7.55498\teval-rmspe:0.999594\ttrain-rmspe:0.999595\n",
      "[9]\teval-rmse:7.47832\ttrain-rmse:7.47959\teval-rmspe:0.999549\ttrain-rmspe:0.99955\n",
      "[10]\teval-rmse:7.40372\ttrain-rmse:7.40496\teval-rmspe:0.999501\ttrain-rmspe:0.999503\n",
      "[11]\teval-rmse:7.32984\ttrain-rmse:7.33107\teval-rmspe:0.99945\ttrain-rmspe:0.999452\n",
      "[12]\teval-rmse:7.25668\ttrain-rmse:7.2579\teval-rmspe:0.999396\ttrain-rmspe:0.999398\n",
      "[13]\teval-rmse:7.18427\ttrain-rmse:7.18549\teval-rmspe:0.999338\ttrain-rmspe:0.99934\n",
      "[14]\teval-rmse:7.1126\ttrain-rmse:7.11383\teval-rmspe:0.999277\ttrain-rmspe:0.999278\n",
      "[15]\teval-rmse:7.04164\ttrain-rmse:7.04285\teval-rmspe:0.999211\ttrain-rmspe:0.999213\n",
      "[16]\teval-rmse:6.97138\ttrain-rmse:6.9726\teval-rmspe:0.999142\ttrain-rmspe:0.999144\n",
      "[17]\teval-rmse:6.90183\ttrain-rmse:6.90304\teval-rmspe:0.999068\ttrain-rmspe:0.99907\n",
      "[18]\teval-rmse:6.83296\ttrain-rmse:6.83416\teval-rmspe:0.99899\ttrain-rmspe:0.998992\n",
      "[19]\teval-rmse:6.76478\ttrain-rmse:6.76598\teval-rmspe:0.998907\ttrain-rmspe:0.99891\n",
      "[20]\teval-rmse:6.69728\ttrain-rmse:6.6985\teval-rmspe:0.99882\ttrain-rmspe:0.998822\n",
      "[21]\teval-rmse:6.6305\ttrain-rmse:6.63169\teval-rmspe:0.998727\ttrain-rmspe:0.99873\n",
      "[22]\teval-rmse:6.56439\ttrain-rmse:6.56556\teval-rmspe:0.998629\ttrain-rmspe:0.998632\n",
      "[23]\teval-rmse:6.49893\ttrain-rmse:6.50009\teval-rmspe:0.998525\ttrain-rmspe:0.998528\n",
      "[24]\teval-rmse:6.43409\ttrain-rmse:6.43524\teval-rmspe:0.998416\ttrain-rmspe:0.998419\n",
      "[25]\teval-rmse:6.36994\ttrain-rmse:6.37106\teval-rmspe:0.998301\ttrain-rmspe:0.998304\n",
      "[26]\teval-rmse:6.30639\ttrain-rmse:6.30752\teval-rmspe:0.998179\ttrain-rmspe:0.998183\n",
      "[27]\teval-rmse:6.24352\ttrain-rmse:6.24464\teval-rmspe:0.99805\ttrain-rmspe:0.998055\n",
      "[28]\teval-rmse:6.18126\ttrain-rmse:6.18237\teval-rmspe:0.997915\ttrain-rmspe:0.99792\n",
      "[29]\teval-rmse:6.11959\ttrain-rmse:6.1207\teval-rmspe:0.997773\ttrain-rmspe:0.997778\n",
      "[30]\teval-rmse:6.05856\ttrain-rmse:6.05966\teval-rmspe:0.997624\ttrain-rmspe:0.997629\n",
      "[31]\teval-rmse:5.99814\ttrain-rmse:5.99925\teval-rmspe:0.997466\ttrain-rmspe:0.997471\n",
      "[32]\teval-rmse:5.93834\ttrain-rmse:5.93944\teval-rmspe:0.9973\ttrain-rmspe:0.997306\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[672]\teval-rmse:0.142684\ttrain-rmse:0.136031\teval-rmspe:0.160307\ttrain-rmspe:0.162474\n",
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     ]
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[960]\teval-rmse:0.116963\ttrain-rmse:0.107696\teval-rmspe:0.135909\ttrain-rmspe:0.127288\n",
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1246]\teval-rmse:0.106704\ttrain-rmse:0.095341\teval-rmspe:0.125273\ttrain-rmspe:0.110391\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1816]\teval-rmse:0.097002\ttrain-rmse:0.081949\teval-rmspe:0.115158\ttrain-rmspe:0.092758\n",
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      "[1873]\teval-rmse:0.096455\ttrain-rmse:0.081063\teval-rmspe:0.114601\ttrain-rmspe:0.091627\n",
      "[1874]\teval-rmse:0.09645\ttrain-rmse:0.081055\teval-rmspe:0.114597\ttrain-rmspe:0.091617\n",
      "[1875]\teval-rmse:0.096441\ttrain-rmse:0.081042\teval-rmspe:0.114589\ttrain-rmspe:0.091604\n",
      "[1876]\teval-rmse:0.096423\ttrain-rmse:0.081015\teval-rmspe:0.114573\ttrain-rmspe:0.091575\n",
      "[1877]\teval-rmse:0.096417\ttrain-rmse:0.081\teval-rmspe:0.114568\ttrain-rmspe:0.091496\n",
      "[1878]\teval-rmse:0.096412\ttrain-rmse:0.08099\teval-rmspe:0.114564\ttrain-rmspe:0.091482\n",
      "[1879]\teval-rmse:0.096402\ttrain-rmse:0.080974\teval-rmspe:0.114555\ttrain-rmspe:0.09146\n",
      "[1880]\teval-rmse:0.096396\ttrain-rmse:0.080962\teval-rmspe:0.11455\ttrain-rmspe:0.091448\n",
      "[1881]\teval-rmse:0.096393\ttrain-rmse:0.080955\teval-rmspe:0.114544\ttrain-rmspe:0.091441\n",
      "[1882]\teval-rmse:0.096382\ttrain-rmse:0.080937\teval-rmspe:0.114535\ttrain-rmspe:0.09142\n",
      "[1883]\teval-rmse:0.096377\ttrain-rmse:0.080929\teval-rmspe:0.114533\ttrain-rmspe:0.091413\n",
      "[1884]\teval-rmse:0.096371\ttrain-rmse:0.080921\teval-rmspe:0.114528\ttrain-rmspe:0.091403\n",
      "[1885]\teval-rmse:0.096366\ttrain-rmse:0.080916\teval-rmspe:0.114523\ttrain-rmspe:0.091398\n",
      "[1886]\teval-rmse:0.096354\ttrain-rmse:0.080892\teval-rmspe:0.114511\ttrain-rmspe:0.091363\n",
      "[1887]\teval-rmse:0.09635\ttrain-rmse:0.080885\teval-rmspe:0.114505\ttrain-rmspe:0.091355\n",
      "[1888]\teval-rmse:0.096341\ttrain-rmse:0.080872\teval-rmspe:0.114495\ttrain-rmspe:0.091342\n",
      "[1889]\teval-rmse:0.096329\ttrain-rmse:0.080847\teval-rmspe:0.114482\ttrain-rmspe:0.091297\n",
      "[1890]\teval-rmse:0.096321\ttrain-rmse:0.080836\teval-rmspe:0.114478\ttrain-rmspe:0.091286\n",
      "[1891]\teval-rmse:0.096316\ttrain-rmse:0.080826\teval-rmspe:0.114471\ttrain-rmspe:0.091275\n",
      "[1892]\teval-rmse:0.096309\ttrain-rmse:0.080812\teval-rmspe:0.114466\ttrain-rmspe:0.091258\n",
      "[1893]\teval-rmse:0.096297\ttrain-rmse:0.080783\teval-rmspe:0.11445\ttrain-rmspe:0.091212\n",
      "[1894]\teval-rmse:0.096285\ttrain-rmse:0.080762\teval-rmspe:0.11444\ttrain-rmspe:0.09119\n",
      "[1895]\teval-rmse:0.096277\ttrain-rmse:0.080746\teval-rmspe:0.114432\ttrain-rmspe:0.091174\n",
      "[1896]\teval-rmse:0.096266\ttrain-rmse:0.080729\teval-rmspe:0.114422\ttrain-rmspe:0.091156\n",
      "[1897]\teval-rmse:0.096257\ttrain-rmse:0.080715\teval-rmspe:0.114415\ttrain-rmspe:0.09114\n",
      "[1898]\teval-rmse:0.096254\ttrain-rmse:0.080707\teval-rmspe:0.114413\ttrain-rmspe:0.091127\n",
      "[1899]\teval-rmse:0.096242\ttrain-rmse:0.080687\teval-rmspe:0.114406\ttrain-rmspe:0.091106\n",
      "[1900]\teval-rmse:0.096237\ttrain-rmse:0.080677\teval-rmspe:0.114401\ttrain-rmspe:0.091095\n",
      "[1901]\teval-rmse:0.096234\ttrain-rmse:0.080672\teval-rmspe:0.114399\ttrain-rmspe:0.091088\n",
      "[1902]\teval-rmse:0.096227\ttrain-rmse:0.080662\teval-rmspe:0.114392\ttrain-rmspe:0.091077\n",
      "[1903]\teval-rmse:0.09621\ttrain-rmse:0.080628\teval-rmspe:0.114372\ttrain-rmspe:0.091017\n",
      "[1904]\teval-rmse:0.0962\ttrain-rmse:0.080611\teval-rmspe:0.114363\ttrain-rmspe:0.090995\n",
      "[1905]\teval-rmse:0.096197\ttrain-rmse:0.080608\teval-rmspe:0.114359\ttrain-rmspe:0.090992\n",
      "[1906]\teval-rmse:0.096186\ttrain-rmse:0.08059\teval-rmspe:0.114349\ttrain-rmspe:0.090971\n",
      "[1907]\teval-rmse:0.096181\ttrain-rmse:0.08058\teval-rmspe:0.114345\ttrain-rmspe:0.09096\n",
      "[1908]\teval-rmse:0.096175\ttrain-rmse:0.080574\teval-rmspe:0.114335\ttrain-rmspe:0.090953\n",
      "[1909]\teval-rmse:0.096164\ttrain-rmse:0.080553\teval-rmspe:0.114319\ttrain-rmspe:0.090909\n",
      "[1910]\teval-rmse:0.096152\ttrain-rmse:0.080537\teval-rmspe:0.11431\ttrain-rmspe:0.090893\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1911]\teval-rmse:0.096145\ttrain-rmse:0.080522\teval-rmspe:0.1143\ttrain-rmspe:0.090875\n",
      "[1912]\teval-rmse:0.096135\ttrain-rmse:0.0805\teval-rmspe:0.114291\ttrain-rmspe:0.090847\n",
      "[1913]\teval-rmse:0.096127\ttrain-rmse:0.080484\teval-rmspe:0.114284\ttrain-rmspe:0.090828\n",
      "[1914]\teval-rmse:0.096124\ttrain-rmse:0.080475\teval-rmspe:0.11428\ttrain-rmspe:0.090815\n",
      "[1915]\teval-rmse:0.096111\ttrain-rmse:0.080447\teval-rmspe:0.114263\ttrain-rmspe:0.090752\n",
      "[1916]\teval-rmse:0.096097\ttrain-rmse:0.080431\teval-rmspe:0.11425\ttrain-rmspe:0.090736\n",
      "[1917]\teval-rmse:0.096084\ttrain-rmse:0.080408\teval-rmspe:0.114229\ttrain-rmspe:0.0907\n",
      "[1918]\teval-rmse:0.096078\ttrain-rmse:0.080398\teval-rmspe:0.114223\ttrain-rmspe:0.090689\n",
      "[1919]\teval-rmse:0.09607\ttrain-rmse:0.080384\teval-rmspe:0.11421\ttrain-rmspe:0.090672\n",
      "[1920]\teval-rmse:0.096064\ttrain-rmse:0.08037\teval-rmspe:0.114204\ttrain-rmspe:0.090652\n",
      "[1921]\teval-rmse:0.096054\ttrain-rmse:0.08035\teval-rmspe:0.114193\ttrain-rmspe:0.09061\n",
      "[1922]\teval-rmse:0.096039\ttrain-rmse:0.080323\teval-rmspe:0.114171\ttrain-rmspe:0.090568\n",
      "[1923]\teval-rmse:0.096033\ttrain-rmse:0.080313\teval-rmspe:0.114167\ttrain-rmspe:0.090552\n",
      "[1924]\teval-rmse:0.096026\ttrain-rmse:0.080305\teval-rmspe:0.114161\ttrain-rmspe:0.090544\n",
      "[1925]\teval-rmse:0.096003\ttrain-rmse:0.080271\teval-rmspe:0.114141\ttrain-rmspe:0.09051\n",
      "[1926]\teval-rmse:0.095998\ttrain-rmse:0.080261\teval-rmspe:0.114136\ttrain-rmspe:0.090501\n",
      "[1927]\teval-rmse:0.095995\ttrain-rmse:0.080256\teval-rmspe:0.114133\ttrain-rmspe:0.090495\n",
      "[1928]\teval-rmse:0.095983\ttrain-rmse:0.080237\teval-rmspe:0.114123\ttrain-rmspe:0.090475\n",
      "[1929]\teval-rmse:0.095975\ttrain-rmse:0.080219\teval-rmspe:0.114117\ttrain-rmspe:0.090453\n",
      "[1930]\teval-rmse:0.095964\ttrain-rmse:0.080198\teval-rmspe:0.114108\ttrain-rmspe:0.090428\n",
      "[1931]\teval-rmse:0.095962\ttrain-rmse:0.080193\teval-rmspe:0.114106\ttrain-rmspe:0.090424\n",
      "[1932]\teval-rmse:0.095951\ttrain-rmse:0.080171\teval-rmspe:0.114084\ttrain-rmspe:0.090389\n",
      "[1933]\teval-rmse:0.095936\ttrain-rmse:0.080147\teval-rmspe:0.114071\ttrain-rmspe:0.090364\n",
      "[1934]\teval-rmse:0.09593\ttrain-rmse:0.080128\teval-rmspe:0.114072\ttrain-rmspe:0.090341\n",
      "[1935]\teval-rmse:0.095926\ttrain-rmse:0.080124\teval-rmspe:0.114069\ttrain-rmspe:0.090336\n",
      "[1936]\teval-rmse:0.095919\ttrain-rmse:0.080108\teval-rmspe:0.114061\ttrain-rmspe:0.090256\n",
      "[1937]\teval-rmse:0.095908\ttrain-rmse:0.080086\teval-rmspe:0.114046\ttrain-rmspe:0.090228\n",
      "[1938]\teval-rmse:0.095904\ttrain-rmse:0.080078\teval-rmspe:0.114042\ttrain-rmspe:0.09022\n",
      "[1939]\teval-rmse:0.095899\ttrain-rmse:0.08007\teval-rmspe:0.114041\ttrain-rmspe:0.090211\n",
      "[1940]\teval-rmse:0.095895\ttrain-rmse:0.080064\teval-rmspe:0.114037\ttrain-rmspe:0.090197\n",
      "[1941]\teval-rmse:0.095885\ttrain-rmse:0.080049\teval-rmspe:0.114028\ttrain-rmspe:0.090181\n",
      "[1942]\teval-rmse:0.095868\ttrain-rmse:0.080026\teval-rmspe:0.114001\ttrain-rmspe:0.090153\n",
      "[1943]\teval-rmse:0.095866\ttrain-rmse:0.080022\teval-rmspe:0.114\ttrain-rmspe:0.090148\n",
      "[1944]\teval-rmse:0.095858\ttrain-rmse:0.08001\teval-rmspe:0.113993\ttrain-rmspe:0.090136\n",
      "[1945]\teval-rmse:0.095852\ttrain-rmse:0.080001\teval-rmspe:0.113985\ttrain-rmspe:0.090127\n",
      "[1946]\teval-rmse:0.095844\ttrain-rmse:0.079987\teval-rmspe:0.113976\ttrain-rmspe:0.09011\n",
      "[1947]\teval-rmse:0.095841\ttrain-rmse:0.079984\teval-rmspe:0.113974\ttrain-rmspe:0.090106\n",
      "[1948]\teval-rmse:0.095834\ttrain-rmse:0.079969\teval-rmspe:0.113962\ttrain-rmspe:0.090084\n",
      "[1949]\teval-rmse:0.095829\ttrain-rmse:0.079959\teval-rmspe:0.113956\ttrain-rmspe:0.090062\n",
      "[1950]\teval-rmse:0.095818\ttrain-rmse:0.07994\teval-rmspe:0.113948\ttrain-rmspe:0.090043\n",
      "[1951]\teval-rmse:0.095803\ttrain-rmse:0.079915\teval-rmspe:0.113929\ttrain-rmspe:0.090015\n",
      "[1952]\teval-rmse:0.09579\ttrain-rmse:0.079896\teval-rmspe:0.113918\ttrain-rmspe:0.089992\n",
      "[1953]\teval-rmse:0.095782\ttrain-rmse:0.079883\teval-rmspe:0.113909\ttrain-rmspe:0.08998\n",
      "[1954]\teval-rmse:0.095777\ttrain-rmse:0.079872\teval-rmspe:0.113905\ttrain-rmspe:0.089967\n",
      "[1955]\teval-rmse:0.095761\ttrain-rmse:0.079842\teval-rmspe:0.113893\ttrain-rmspe:0.089932\n",
      "[1956]\teval-rmse:0.095756\ttrain-rmse:0.07983\teval-rmspe:0.113888\ttrain-rmspe:0.089917\n",
      "[1957]\teval-rmse:0.095748\ttrain-rmse:0.079814\teval-rmspe:0.113881\ttrain-rmspe:0.089899\n",
      "[1958]\teval-rmse:0.095744\ttrain-rmse:0.079806\teval-rmspe:0.113877\ttrain-rmspe:0.08989\n",
      "[1959]\teval-rmse:0.095736\ttrain-rmse:0.07979\teval-rmspe:0.11387\ttrain-rmspe:0.089869\n",
      "[1960]\teval-rmse:0.095729\ttrain-rmse:0.079778\teval-rmspe:0.113862\ttrain-rmspe:0.089857\n",
      "[1961]\teval-rmse:0.095722\ttrain-rmse:0.079762\teval-rmspe:0.113856\ttrain-rmspe:0.08984\n",
      "[1962]\teval-rmse:0.095718\ttrain-rmse:0.079756\teval-rmspe:0.113851\ttrain-rmspe:0.089834\n",
      "[1963]\teval-rmse:0.095712\ttrain-rmse:0.079743\teval-rmspe:0.113845\ttrain-rmspe:0.089808\n",
      "[1964]\teval-rmse:0.095705\ttrain-rmse:0.079732\teval-rmspe:0.113838\ttrain-rmspe:0.089788\n",
      "[1965]\teval-rmse:0.0957\ttrain-rmse:0.07972\teval-rmspe:0.113832\ttrain-rmspe:0.089776\n",
      "[1966]\teval-rmse:0.095695\ttrain-rmse:0.079712\teval-rmspe:0.113827\ttrain-rmspe:0.089767\n",
      "[1967]\teval-rmse:0.095686\ttrain-rmse:0.079698\teval-rmspe:0.113818\ttrain-rmspe:0.089751\n",
      "[1968]\teval-rmse:0.095681\ttrain-rmse:0.079681\teval-rmspe:0.113812\ttrain-rmspe:0.089731\n",
      "[1969]\teval-rmse:0.095676\ttrain-rmse:0.079668\teval-rmspe:0.113806\ttrain-rmspe:0.089717\n",
      "[1970]\teval-rmse:0.095668\ttrain-rmse:0.079659\teval-rmspe:0.113796\ttrain-rmspe:0.089705\n",
      "[1971]\teval-rmse:0.09565\ttrain-rmse:0.079627\teval-rmspe:0.113764\ttrain-rmspe:0.089651\n",
      "[1972]\teval-rmse:0.095642\ttrain-rmse:0.079615\teval-rmspe:0.113763\ttrain-rmspe:0.089639\n",
      "[1973]\teval-rmse:0.095636\ttrain-rmse:0.0796\teval-rmspe:0.11376\ttrain-rmspe:0.089622\n",
      "[1974]\teval-rmse:0.095633\ttrain-rmse:0.079596\teval-rmspe:0.113756\ttrain-rmspe:0.089617\n",
      "[1975]\teval-rmse:0.095627\ttrain-rmse:0.079584\teval-rmspe:0.11375\ttrain-rmspe:0.089566\n",
      "[1976]\teval-rmse:0.095616\ttrain-rmse:0.079562\teval-rmspe:0.113736\ttrain-rmspe:0.089537\n",
      "[1977]\teval-rmse:0.095605\ttrain-rmse:0.079547\teval-rmspe:0.113725\ttrain-rmspe:0.089523\n",
      "[1978]\teval-rmse:0.095603\ttrain-rmse:0.07954\teval-rmspe:0.113721\ttrain-rmspe:0.089515\n",
      "[1979]\teval-rmse:0.095598\ttrain-rmse:0.079526\teval-rmspe:0.113717\ttrain-rmspe:0.089443\n",
      "[1980]\teval-rmse:0.09559\ttrain-rmse:0.079514\teval-rmspe:0.113709\ttrain-rmspe:0.08943\n",
      "[1981]\teval-rmse:0.095583\ttrain-rmse:0.079502\teval-rmspe:0.113703\ttrain-rmspe:0.089415\n",
      "[1982]\teval-rmse:0.095569\ttrain-rmse:0.079484\teval-rmspe:0.113691\ttrain-rmspe:0.089392\n",
      "[1983]\teval-rmse:0.095562\ttrain-rmse:0.079473\teval-rmspe:0.113683\ttrain-rmspe:0.089365\n",
      "[1984]\teval-rmse:0.095555\ttrain-rmse:0.079454\teval-rmspe:0.113679\ttrain-rmspe:0.089344\n",
      "[1985]\teval-rmse:0.095544\ttrain-rmse:0.079432\teval-rmspe:0.113671\ttrain-rmspe:0.089263\n",
      "[1986]\teval-rmse:0.095524\ttrain-rmse:0.079403\teval-rmspe:0.113654\ttrain-rmspe:0.089234\n",
      "[1987]\teval-rmse:0.095515\ttrain-rmse:0.07939\teval-rmspe:0.113645\ttrain-rmspe:0.08922\n",
      "[1988]\teval-rmse:0.09551\ttrain-rmse:0.079381\teval-rmspe:0.11364\ttrain-rmspe:0.089209\n",
      "[1989]\teval-rmse:0.095493\ttrain-rmse:0.079354\teval-rmspe:0.113626\ttrain-rmspe:0.089177\n",
      "[1990]\teval-rmse:0.095487\ttrain-rmse:0.079337\teval-rmspe:0.11362\ttrain-rmspe:0.08916\n",
      "[1991]\teval-rmse:0.095481\ttrain-rmse:0.079325\teval-rmspe:0.113614\ttrain-rmspe:0.089148\n",
      "[1992]\teval-rmse:0.095475\ttrain-rmse:0.079312\teval-rmspe:0.11361\ttrain-rmspe:0.089134\n",
      "[1993]\teval-rmse:0.095467\ttrain-rmse:0.079295\teval-rmspe:0.113594\ttrain-rmspe:0.089116\n",
      "[1994]\teval-rmse:0.09546\ttrain-rmse:0.079284\teval-rmspe:0.113588\ttrain-rmspe:0.089106\n",
      "[1995]\teval-rmse:0.095457\ttrain-rmse:0.079276\teval-rmspe:0.113586\ttrain-rmspe:0.089097\n",
      "[1996]\teval-rmse:0.095445\ttrain-rmse:0.079247\teval-rmspe:0.113571\ttrain-rmspe:0.089056\n",
      "[1997]\teval-rmse:0.095435\ttrain-rmse:0.079228\teval-rmspe:0.113562\ttrain-rmspe:0.08903\n",
      "[1998]\teval-rmse:0.095428\ttrain-rmse:0.079218\teval-rmspe:0.113555\ttrain-rmspe:0.089019\n",
      "[1999]\teval-rmse:0.095419\ttrain-rmse:0.079198\teval-rmspe:0.113546\ttrain-rmspe:0.088999\n",
      "[2000]\teval-rmse:0.09541\ttrain-rmse:0.079182\teval-rmspe:0.113539\ttrain-rmspe:0.088981\n",
      "[2001]\teval-rmse:0.095401\ttrain-rmse:0.07916\teval-rmspe:0.11353\ttrain-rmspe:0.088958\n",
      "[2002]\teval-rmse:0.095397\ttrain-rmse:0.079151\teval-rmspe:0.113527\ttrain-rmspe:0.088941\n",
      "[2003]\teval-rmse:0.095392\ttrain-rmse:0.079141\teval-rmspe:0.113523\ttrain-rmspe:0.088928\n",
      "[2004]\teval-rmse:0.09538\ttrain-rmse:0.079121\teval-rmspe:0.113513\ttrain-rmspe:0.088907\n",
      "[2005]\teval-rmse:0.095372\ttrain-rmse:0.079109\teval-rmspe:0.113492\ttrain-rmspe:0.088892\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2006]\teval-rmse:0.095357\ttrain-rmse:0.07909\teval-rmspe:0.113477\ttrain-rmspe:0.088871\n",
      "[2007]\teval-rmse:0.09535\ttrain-rmse:0.079078\teval-rmspe:0.11347\ttrain-rmspe:0.088858\n",
      "[2008]\teval-rmse:0.095342\ttrain-rmse:0.07906\teval-rmspe:0.113458\ttrain-rmspe:0.088831\n",
      "[2009]\teval-rmse:0.095332\ttrain-rmse:0.079039\teval-rmspe:0.113438\ttrain-rmspe:0.088798\n",
      "[2010]\teval-rmse:0.095328\ttrain-rmse:0.079031\teval-rmspe:0.113434\ttrain-rmspe:0.088789\n",
      "[2011]\teval-rmse:0.09532\ttrain-rmse:0.079016\teval-rmspe:0.113426\ttrain-rmspe:0.088775\n",
      "[2012]\teval-rmse:0.095303\ttrain-rmse:0.078992\teval-rmspe:0.113411\ttrain-rmspe:0.08875\n",
      "[2013]\teval-rmse:0.095289\ttrain-rmse:0.07897\teval-rmspe:0.113399\ttrain-rmspe:0.088722\n",
      "[2014]\teval-rmse:0.095284\ttrain-rmse:0.078962\teval-rmspe:0.11339\ttrain-rmspe:0.088712\n",
      "[2015]\teval-rmse:0.095281\ttrain-rmse:0.078956\teval-rmspe:0.113387\ttrain-rmspe:0.088705\n",
      "[2016]\teval-rmse:0.095269\ttrain-rmse:0.078932\teval-rmspe:0.113377\ttrain-rmspe:0.088669\n",
      "[2017]\teval-rmse:0.095258\ttrain-rmse:0.07892\teval-rmspe:0.113366\ttrain-rmspe:0.088657\n",
      "[2018]\teval-rmse:0.095256\ttrain-rmse:0.078916\teval-rmspe:0.113364\ttrain-rmspe:0.088651\n",
      "[2019]\teval-rmse:0.09525\ttrain-rmse:0.078903\teval-rmspe:0.113359\ttrain-rmspe:0.088627\n",
      "[2020]\teval-rmse:0.095245\ttrain-rmse:0.078891\teval-rmspe:0.113355\ttrain-rmspe:0.088615\n",
      "[2021]\teval-rmse:0.095233\ttrain-rmse:0.078872\teval-rmspe:0.113344\ttrain-rmspe:0.08859\n",
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      "[2023]\teval-rmse:0.095227\ttrain-rmse:0.078859\teval-rmspe:0.113342\ttrain-rmspe:0.088571\n",
      "[2024]\teval-rmse:0.095222\ttrain-rmse:0.078844\teval-rmspe:0.113336\ttrain-rmspe:0.088555\n",
      "[2025]\teval-rmse:0.095213\ttrain-rmse:0.078832\teval-rmspe:0.113326\ttrain-rmspe:0.088543\n",
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      "[2027]\teval-rmse:0.095209\ttrain-rmse:0.078822\teval-rmspe:0.113323\ttrain-rmspe:0.088531\n",
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      "[2031]\teval-rmse:0.095164\ttrain-rmse:0.078757\teval-rmspe:0.113282\ttrain-rmspe:0.088454\n",
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      "[2035]\teval-rmse:0.095148\ttrain-rmse:0.078726\teval-rmspe:0.113271\ttrain-rmspe:0.088414\n",
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      "[2037]\teval-rmse:0.095128\ttrain-rmse:0.078698\teval-rmspe:0.113256\ttrain-rmspe:0.088386\n",
      "[2038]\teval-rmse:0.095118\ttrain-rmse:0.078682\teval-rmspe:0.113245\ttrain-rmspe:0.08837\n",
      "[2039]\teval-rmse:0.095108\ttrain-rmse:0.078662\teval-rmspe:0.113236\ttrain-rmspe:0.08834\n",
      "[2040]\teval-rmse:0.095106\ttrain-rmse:0.078655\teval-rmspe:0.113234\ttrain-rmspe:0.088331\n",
      "[2041]\teval-rmse:0.095098\ttrain-rmse:0.078641\teval-rmspe:0.113228\ttrain-rmspe:0.08831\n",
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      "[2043]\teval-rmse:0.095077\ttrain-rmse:0.078606\teval-rmspe:0.113206\ttrain-rmspe:0.088269\n",
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      "[2046]\teval-rmse:0.095054\ttrain-rmse:0.078576\teval-rmspe:0.113183\ttrain-rmspe:0.088235\n",
      "[2047]\teval-rmse:0.095048\ttrain-rmse:0.07857\teval-rmspe:0.113176\ttrain-rmspe:0.088229\n",
      "[2048]\teval-rmse:0.095036\ttrain-rmse:0.078551\teval-rmspe:0.113164\ttrain-rmspe:0.088209\n",
      "[2049]\teval-rmse:0.095021\ttrain-rmse:0.078536\teval-rmspe:0.113149\ttrain-rmspe:0.088196\n",
      "[2050]\teval-rmse:0.095019\ttrain-rmse:0.078532\teval-rmspe:0.113146\ttrain-rmspe:0.088157\n",
      "[2051]\teval-rmse:0.095012\ttrain-rmse:0.078517\teval-rmspe:0.113141\ttrain-rmspe:0.088142\n",
      "[2052]\teval-rmse:0.095007\ttrain-rmse:0.078502\teval-rmspe:0.113137\ttrain-rmspe:0.088115\n",
      "[2053]\teval-rmse:0.095001\ttrain-rmse:0.078494\teval-rmspe:0.113131\ttrain-rmspe:0.088107\n",
      "[2054]\teval-rmse:0.094986\ttrain-rmse:0.078467\teval-rmspe:0.113111\ttrain-rmspe:0.088056\n",
      "[2055]\teval-rmse:0.094982\ttrain-rmse:0.078455\teval-rmspe:0.113109\ttrain-rmspe:0.088039\n",
      "[2056]\teval-rmse:0.094979\ttrain-rmse:0.078448\teval-rmspe:0.113106\ttrain-rmspe:0.088031\n",
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      "[2058]\teval-rmse:0.09497\ttrain-rmse:0.078431\teval-rmspe:0.113087\ttrain-rmspe:0.087961\n",
      "[2059]\teval-rmse:0.094955\ttrain-rmse:0.078407\teval-rmspe:0.113067\ttrain-rmspe:0.087933\n",
      "[2060]\teval-rmse:0.094949\ttrain-rmse:0.078392\teval-rmspe:0.11306\ttrain-rmspe:0.087912\n",
      "[2061]\teval-rmse:0.094939\ttrain-rmse:0.078377\teval-rmspe:0.113041\ttrain-rmspe:0.087892\n",
      "[2062]\teval-rmse:0.094936\ttrain-rmse:0.07837\teval-rmspe:0.113037\ttrain-rmspe:0.087885\n",
      "[2063]\teval-rmse:0.094933\ttrain-rmse:0.078363\teval-rmspe:0.113036\ttrain-rmspe:0.087876\n",
      "[2064]\teval-rmse:0.09492\ttrain-rmse:0.078343\teval-rmspe:0.113024\ttrain-rmspe:0.087853\n",
      "[2065]\teval-rmse:0.094908\ttrain-rmse:0.078318\teval-rmspe:0.112999\ttrain-rmspe:0.087806\n",
      "[2066]\teval-rmse:0.094893\ttrain-rmse:0.078298\teval-rmspe:0.112983\ttrain-rmspe:0.087782\n",
      "[2067]\teval-rmse:0.09489\ttrain-rmse:0.078293\teval-rmspe:0.112981\ttrain-rmspe:0.087777\n",
      "[2068]\teval-rmse:0.094881\ttrain-rmse:0.078281\teval-rmspe:0.112973\ttrain-rmspe:0.087765\n",
      "[2069]\teval-rmse:0.094879\ttrain-rmse:0.078277\teval-rmspe:0.112971\ttrain-rmspe:0.087761\n",
      "[2070]\teval-rmse:0.094875\ttrain-rmse:0.07827\teval-rmspe:0.112968\ttrain-rmspe:0.087746\n",
      "[2071]\teval-rmse:0.094862\ttrain-rmse:0.078252\teval-rmspe:0.112956\ttrain-rmspe:0.087728\n",
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      "[2086]\teval-rmse:0.094757\ttrain-rmse:0.078058\teval-rmspe:0.112838\ttrain-rmspe:0.087496\n",
      "[2087]\teval-rmse:0.094751\ttrain-rmse:0.078044\teval-rmspe:0.112834\ttrain-rmspe:0.087478\n",
      "[2088]\teval-rmse:0.094744\ttrain-rmse:0.078027\teval-rmspe:0.112828\ttrain-rmspe:0.087454\n",
      "[2089]\teval-rmse:0.094736\ttrain-rmse:0.078011\teval-rmspe:0.11282\ttrain-rmspe:0.087434\n",
      "[2090]\teval-rmse:0.094729\ttrain-rmse:0.077994\teval-rmspe:0.112809\ttrain-rmspe:0.087405\n",
      "[2091]\teval-rmse:0.094724\ttrain-rmse:0.077976\teval-rmspe:0.112807\ttrain-rmspe:0.087371\n",
      "[2092]\teval-rmse:0.094723\ttrain-rmse:0.077974\teval-rmspe:0.112806\ttrain-rmspe:0.087368\n",
      "[2093]\teval-rmse:0.094722\ttrain-rmse:0.077971\teval-rmspe:0.112805\ttrain-rmspe:0.087365\n",
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      "[2095]\teval-rmse:0.094712\ttrain-rmse:0.077952\teval-rmspe:0.112793\ttrain-rmspe:0.087346\n",
      "[2096]\teval-rmse:0.094707\ttrain-rmse:0.077941\teval-rmspe:0.112787\ttrain-rmspe:0.087329\n",
      "[2097]\teval-rmse:0.094697\ttrain-rmse:0.077927\teval-rmspe:0.112777\ttrain-rmspe:0.087313\n",
      "[2098]\teval-rmse:0.094685\ttrain-rmse:0.077907\teval-rmspe:0.112766\ttrain-rmspe:0.087291\n",
      "[2099]\teval-rmse:0.094682\ttrain-rmse:0.077897\teval-rmspe:0.112762\ttrain-rmspe:0.087277\n",
      "[2100]\teval-rmse:0.094672\ttrain-rmse:0.07788\teval-rmspe:0.112753\ttrain-rmspe:0.087258\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2101]\teval-rmse:0.094664\ttrain-rmse:0.077869\teval-rmspe:0.112748\ttrain-rmspe:0.087247\n",
      "[2102]\teval-rmse:0.094655\ttrain-rmse:0.077856\teval-rmspe:0.11274\ttrain-rmspe:0.087234\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2386]\teval-rmse:0.092931\ttrain-rmse:0.074445\teval-rmspe:0.110993\ttrain-rmspe:0.082508\n",
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     ]
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2766]\teval-rmse:0.091284\ttrain-rmse:0.070635\teval-rmspe:0.109283\ttrain-rmspe:0.077491\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3051]\teval-rmse:0.090417\ttrain-rmse:0.068136\teval-rmspe:0.108366\ttrain-rmspe:0.074066\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3621]\teval-rmse:0.089222\ttrain-rmse:0.063851\teval-rmspe:0.107093\ttrain-rmspe:0.068481\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3906]\teval-rmse:0.08886\ttrain-rmse:0.061941\teval-rmspe:0.106685\ttrain-rmspe:0.065965\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[4965]\teval-rmse:0.088067\ttrain-rmse:0.055895\teval-rmspe:0.105603\ttrain-rmspe:0.058426\n",
      "[4966]\teval-rmse:0.088067\ttrain-rmse:0.05589\teval-rmspe:0.105604\ttrain-rmspe:0.058422\n",
      "[4967]\teval-rmse:0.088067\ttrain-rmse:0.055885\teval-rmspe:0.105603\ttrain-rmspe:0.058416\n",
      "[4968]\teval-rmse:0.088066\ttrain-rmse:0.055883\teval-rmspe:0.105603\ttrain-rmspe:0.058413\n",
      "[4969]\teval-rmse:0.088066\ttrain-rmse:0.055877\teval-rmspe:0.105603\ttrain-rmspe:0.058407\n",
      "[4970]\teval-rmse:0.088066\ttrain-rmse:0.055872\teval-rmspe:0.105603\ttrain-rmspe:0.058401\n",
      "[4971]\teval-rmse:0.088066\ttrain-rmse:0.055867\teval-rmspe:0.105603\ttrain-rmspe:0.058395\n",
      "[4972]\teval-rmse:0.088064\ttrain-rmse:0.055859\teval-rmspe:0.105594\ttrain-rmspe:0.058382\n",
      "[4973]\teval-rmse:0.088063\ttrain-rmse:0.055851\teval-rmspe:0.105595\ttrain-rmspe:0.058374\n",
      "[4974]\teval-rmse:0.088063\ttrain-rmse:0.055849\teval-rmspe:0.105595\ttrain-rmspe:0.058372\n",
      "[4975]\teval-rmse:0.088063\ttrain-rmse:0.055843\teval-rmspe:0.105594\ttrain-rmspe:0.058361\n",
      "[4976]\teval-rmse:0.088063\ttrain-rmse:0.05584\teval-rmspe:0.105595\ttrain-rmspe:0.058358\n",
      "[4977]\teval-rmse:0.088063\ttrain-rmse:0.055837\teval-rmspe:0.105594\ttrain-rmspe:0.058355\n",
      "[4978]\teval-rmse:0.088062\ttrain-rmse:0.055827\teval-rmspe:0.105594\ttrain-rmspe:0.05834\n",
      "[4979]\teval-rmse:0.088063\ttrain-rmse:0.055819\teval-rmspe:0.105593\ttrain-rmspe:0.058331\n",
      "[4980]\teval-rmse:0.088062\ttrain-rmse:0.055814\teval-rmspe:0.105593\ttrain-rmspe:0.058326\n",
      "[4981]\teval-rmse:0.088062\ttrain-rmse:0.055812\teval-rmspe:0.105593\ttrain-rmspe:0.058324\n",
      "[4982]\teval-rmse:0.088062\ttrain-rmse:0.055808\teval-rmspe:0.105596\ttrain-rmspe:0.058318\n",
      "[4983]\teval-rmse:0.088063\ttrain-rmse:0.055805\teval-rmspe:0.105597\ttrain-rmspe:0.058315\n",
      "[4984]\teval-rmse:0.088062\ttrain-rmse:0.055797\teval-rmspe:0.105597\ttrain-rmspe:0.058306\n",
      "[4985]\teval-rmse:0.088062\ttrain-rmse:0.055789\teval-rmspe:0.1056\ttrain-rmspe:0.058296\n",
      "[4986]\teval-rmse:0.088061\ttrain-rmse:0.055782\teval-rmspe:0.1056\ttrain-rmspe:0.058287\n",
      "[4987]\teval-rmse:0.08806\ttrain-rmse:0.055779\teval-rmspe:0.105601\ttrain-rmspe:0.058284\n",
      "[4988]\teval-rmse:0.088058\ttrain-rmse:0.055772\teval-rmspe:0.105589\ttrain-rmspe:0.058275\n",
      "[4989]\teval-rmse:0.088058\ttrain-rmse:0.055764\teval-rmspe:0.105591\ttrain-rmspe:0.058264\n",
      "[4990]\teval-rmse:0.088057\ttrain-rmse:0.055757\teval-rmspe:0.105587\ttrain-rmspe:0.058254\n",
      "[4991]\teval-rmse:0.088057\ttrain-rmse:0.055754\teval-rmspe:0.105587\ttrain-rmspe:0.058251\n",
      "[4992]\teval-rmse:0.088057\ttrain-rmse:0.055754\teval-rmspe:0.105587\ttrain-rmspe:0.05825\n",
      "[4993]\teval-rmse:0.088056\ttrain-rmse:0.055751\teval-rmspe:0.105586\ttrain-rmspe:0.058248\n",
      "[4994]\teval-rmse:0.088057\ttrain-rmse:0.05575\teval-rmspe:0.105587\ttrain-rmspe:0.058246\n",
      "[4995]\teval-rmse:0.088056\ttrain-rmse:0.055748\teval-rmspe:0.105587\ttrain-rmspe:0.058245\n",
      "[4996]\teval-rmse:0.088058\ttrain-rmse:0.055745\teval-rmspe:0.105589\ttrain-rmspe:0.058241\n",
      "[4997]\teval-rmse:0.088058\ttrain-rmse:0.055738\teval-rmspe:0.105589\ttrain-rmspe:0.058232\n",
      "[4998]\teval-rmse:0.088055\ttrain-rmse:0.055729\teval-rmspe:0.105586\ttrain-rmspe:0.058222\n",
      "[4999]\teval-rmse:0.088055\ttrain-rmse:0.055722\teval-rmspe:0.105584\ttrain-rmspe:0.058213\n",
      "0.10558389497405071\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/Xs+f8tbQPy2JZ392QsTc1dsaCgAR6RT2Vvv4xZylzF9VzHdHHcPdF4yiawTd1dsa+umISIdXW+fnqv9dxKfrd/G77w1j1snZEXdXb2soAESkQ3POcdNLy/hk7U7uuWAU/zK+n9cldRid90HXIhIR/pi3hpcXb+WXZw3Wxr+FFAAi0mH947NN/OXD9VwyIYtrzxjkdTkdjgJARDqkt1cUcevcAiYP7cUdM4arzb8VQgoAM5tmZmvMbJ2ZzW5i+kQzW2xmPjO7sNG0WWa2Nvg1q60KF5HItXBTKdfNWcKYfik8cMk4oqO0L9sazf7UzCwKeAg4GxgGXGJmwxrNthn4EfBMo2VTgVuBE4AJwK1mFpnPXRWRNrG2uIKfPplPZkoCf581noRY3d3bWqHE5gRgnXNug3OuBpgDzGg4g3Nuk3NuGeBvtOxU4F3nXKlzbjfwLjCtDeoWkQhUVFbFrMcWEBPVhSd/PCGiH+XcFkIJgAxgS4P3hcFxoTiSZUVE6pVX1fKjxxdQtr+WJy4fT7/URK9L6vBCCYCmzqy4ENcf0rJmdoWZ5ZtZfklJSYirFpFIUe2r4+f/WMS6HXt5+IfHMyIj2euSOoVQAqAQaHhxbSawLcT1h7Ssc+5R51yucy43PT09xFWLSCTw+x3//sIyPtuwi3suHMWpOdpGtJVQAmAhkGNm/c0sFpgJzA1x/XnAFDPrETz5OyU4TkQkJL+ft4rXv9zGTdOO4/xxmV6X06k0GwDOOR9wDYEN9yrgeedcgZndbmbTAcxsvJkVAhcBj5hZQXDZUuAOAiGyELg9OE5EpFl/+2QDf/1kI7NOOpYrTxvgdTmdjjkXanN++8jNzXX5+flelyEiHnv9y21c++wSzh7RhwcvHRfx/fc2x8wWOedyW7KM7p4QkbDz6fqd/Or5L5mQncqfLx6jjf9RogAQkbCyuqicn/9jEcf2TOSvl+USH6MbvY4WBYCIhI2te/Yz67EFJMZF8cSPJ5CcGON1SZ2aAkBEwkJZZS0/emwBldV1PPnjCWSkJHhdUqenDmFExHNVtXX87B/5fL2rkid+PJ7j+nT3uqSIoAAQEU/V+R03PLeUBZtKeeCSsZw8MM3rkiKGmoBExDPOOW5/vYB5K4r47blD+d7ovl6XFFEUACLimUc+3sCTn33NT7/Tn5+eqhu92psCQEQ88fLiQu6et5rvje7Lb84Z6nU5EUnnAESkXZVX1fLQB+v4+ycbOWlAT/500Si66EYvTygARKRd+Or8zFm4hT+/+xW79tVwwbhMbp0+jLho3ejlFQWAiBx1H39Vwp1vruSr4r1M6J/KE+cOY2SmnunvNQWAiBw163ZUcOebq/hwTQlZqYk8/K/jmDq8D2Zq8gkHCgARaXOl+2q4d/5XPP3FZhJjo7j5nKFcdvKxau4JMwoAEWkz1b46/vHp19z//loqa+r4wQlZXH9mDj27xnldmjRBASAiR8w5R15BEb+ft5qvd1UyaUg6N58zlJze3bwuTQ5DASAiR2R5YRl3vLmSBRtLGdy7K0/+eAKnDVa/vR2BAkBEWqWorIo/5q3h5SWFpCbGcuf3RzBzfD+io3R/aUehABCRFqms8fHoxxt45KMN1PkdV0wcwNWnD6J7vJ7d39EoAEQkJH6/45UlW/lj3hqKyqs4d+QxzD77OPqlJnpdmrSSAkBEmrVwUyl3vLGSZYVljMpM5oFLxzI+O9XrsuQIKQBE5LA+37CLS/76OX26x/Pni0czY3SGnt3TSSgAROSQ9lX7uPHFL8lKTeTN606la5w2GZ2Jfpsickh3z1tN4e79PHfFSdr4d0K6XktEmvTpup089fnXXH5yfyb0V3t/Z6QAEJFv2Vvt48YXl9E/LYkbpw7xuhw5SnRMJyLf8vu3VrGtbD8v/PwkEmL1ALfOSkcAInKQf67dydNfbOan3+lPri717NQUACJSr6KqlpteWsaA9CR+NUVNP52dmoBEpN5db61me9l+XrjyZOJj1PTT2ekIQESAQLeNzy7YzM9OHcDxx/bwuhxpBwoAEaG8qpbZLy1jYHoSN5w12OtypJ2oCUhE+K83VlFUXsVLV6npJ5LoCEAkwn24ZgfP5W/hiokDGZulpp9IogAQiWBl+2uZ/dJycnp15ReTc7wuR9qZmoBEItidb6ykZG81j/zweDX9RCAdAYhEqPdXF/PCokJ+PnEAo/uleF2OeEABIBKByipr+fXLyxncuyvXq+knYqkJSCQC3fZGATv31vC3y8YTF62mn0gV0hGAmU0zszVmts7MZjcxPc7MngtO/8LMsoPjs81sv5ktDX493Lbli0hLzV9ZzMuLt/JvkwYyMjPZ63LEQ80eAZhZFPAQcBZQCCw0s7nOuZUNZvsJsNs5N8jMZgJ/AC4OTlvvnBvTxnWLSCvsqazh168s57g+3bj2DDX9RLpQjgAmAOuccxucczXAHGBGo3lmAE8Gh18EzjQzdRoqEmZue30lu/fV8KeLRhMbrVOAkS6Uv4AMYEuD94XBcU3O45zzAWVAz+C0/ma2xMw+MrNTj7BeEWmldwqKeGXJVv7t9EGMyFDTj4R2EripPXkX4jzbgSzn3C4zOx541cyGO+fKD1rY7ArgCoCsrKwQShKRlti9r4bfvLKCocd055rTB3ldjoSJUI4ACoF+Dd5nAtsONY+ZRQPJQKlzrto5twvAObcIWA9860lTzrlHnXO5zrnc9PT0ln8XInJYv3u9gD2VNfzpolFq+pF6ofwlLARyzKy/mcUCM4G5jeaZC8wKDl8IvO+cc2aWHjyJjJkNAHKADW1TuoiE4u0VRby2dBvXnDGI4X3V9CPfaLYJyDnnM7NrgDwgCnjMOVdgZrcD+c65ucDfgafMbB1QSiAkACYCt5uZD6gDrnTOlR6Nb0REvq10Xw2/fXU5w47pztVq+pFGQroRzDn3FvBWo3G3NBiuAi5qYrmXgJeOsEYRaaVbXltB2f5anvrJCcREqelHDqa/CJFO6q3l23lj2XauOyOHocd097ocCUMKAJFOaNfeav7z1RWMyOjOlZMGel2OhCk9C0ikE7rltQLKq2p5+iI1/cih6S9DpJN5Y9k23ly+nV9MHsxxfdT0I4emIwCRI+Cco9rnp6LKx95qH3urfFRU1wbeHxhX7QtOrw1Mr/JRUf3NdF+dP7AuwDlwuOBr4P2B+y6/Gee+mTc4fGAFDthfW8eozGR+PnFAO/80pKNRAEjEqfbVUVldx95qH5U1B1597KuuY19weG91XfDVR2V1HftqfOyrDsxTUe2joqq2foPv8ze+Mf7bYqKMbvExdIuPpmtc4KtvSjxJcdHERHWpv5XeDAwLvBoQHA4MNZoONHzk1oFpsdFd+OFJxxKtph9phgJAOp3lhWU8l7+ZjTv3BTbk1cGNd01go15b1/wGGwIb1KTYaJLiokiKjSYxLoqucdFkpCTQLb7bNxvz+Gi6BV+7xsXQNS6abvHRB03XM/clHCkApFOoqKrltaXbeHbBZgq2lRMf04XhfZNJToihb3JgTzspNorE4N53YmxUcNw3G/bE2AOvgY1+QkwUeqitdGYKAOmwnHMs2bKHOQs28/qX29lfW8fQY7pzx4zhTB+TQXJCjNclioQ1BYB0OGWVtbyypJA5C7ewuqiCxNgoZozpyyUTshiVmay9dpEQKQCkQ3DOkf/1bp79YjNvLt9Otc/PqMxk7jpvJNPH9KVrnP6URVpK/zUS1nbvq+GlxYG9/XU79tItLpqLcjOZOT5LnZqIHCEFgIQd5xyfbdjFnAVbeHtFETV1fsZlpXDPhaP47qhjSIzVn61IW9B/koSNnXureXFRIXMWbGbTrkq6x0dz6QlZXDIhiyF9unldnkinowAQT+yr9lFUXkVxWRXby6p4b3Ux7xQU4/M7JmSncv3kHM4ecQzxMbp+XuRoUQBIm6rzO3buraaorCqwgS+v+tbwjvJqKqp9By3XIzGGy0/J5uLxWQzq1dWj6kUiiwJAWmRPZQ0rt5VTVF5VvwcfGK6muKyKkr3V1DV6NEJ0F6NXtzh6J8czuHc3Ts1Jp09yPL27x9G7ezx9useT2SNRfdWKtDMFgDSrts7PB6t38PLirby3uvigRyl0i4+mT/d4+iTHk9MrjT7d4+mdHNioB4bj6JkUR1QXXZsvEm4UANIk5xwF28p5cVEhc7/cRum+GtK6xnLZSdmccVwvjkkObPR1RY5Ix6X/XjlIcXkVry7ZykuLC/mqeC+xUV04a1hvzh+XwcTB6epcRKQTUQAIVbV15BUU8dLirfxzbQl+B2OzUrjz+yP43qi+JCfqmToinZECIEI551i4aTcvLy7kzWXbqaj2kZGSwL9NGsT54zIYkK4rcUQ6OwVAhNm8q5KXlxTy8uKtbC6tJDE2irNHHMMFx2dwYv+edNHJWpGIoQCIAOVVtcxbvp2XFm1lwaZSzODkgT35xeQcpg7vQ5IepCYSkfSfH4acc9TU+amtc9T4/NTW+anx+YPj/A3GBec78L7hfL7A8su3lpFXUES1z8+A9CRunDqE74/NICMlwetvU0Q8pgDwmK/Oz/urd/DMgs0s3Fhav+FvK8kJMVyUm8kF4zIZ0y9Fz8oXkXoKAI9s27OfOQu38PzCLRSVV9G7exznj8uka3w0sVFdiI3uQmxUF2KijJjg8DfjujQYZ8RGRRETbcRENZovugsJMVG6CUtEmqQAaEd1fsdHX+3gmS828/7qHThgYk46t80YzpnH9SJa19iLSDtSALSD4vIqnlu4hecWbmHrnv2kdY3jqkkDmTk+i36piV6XJyIRSgFwlPj9jk/W7eTpz7/mvdU7qPM7vjMojd+eO5TJw3rrjloR8ZwCoI2VVFTzfP4W5izczJbS/fRMiuWnp/bnkvFZZKcleV2eiEg9BUAb8Psdn67fxTMLvq7v1OTEAan8x9TjmDK8N3HR6tRERMKPAuAI7Ap2YfhssAvDlMQYfnRyNpeckMVAPUpBRMKcAiBE5VW1bN29P/C1Zz/5X+8mL9hh+fjsHvxi8mCmjeijLgxFpMNQABC483bXvpr6jfuB18L695WUVx3chWFyQgyXnpDFpSdkMbi3OiwXkY4nIgKgzu8oLq865MZ96579VNX6D1qma1w0GSkJZPZIYHx2DzJSEsjokVD/mpYUpweniUiH1ukCoKq2jpXbyynYWsbyrWWs2FrO2h0V33q8QmpSLBkpCeT06sbpQ3odtHHPTEmke0K0HpsgIp1ahw6AyhofK7eV12/oV2wtY13J3vpOyXskxjAiI5lTB/enX49EMnsE9uj7piSoK0MRiXgdZitYUVVbv7EvCL6uL9mLC+7Yp3WNZURGMlOG92ZERjIjMpLpmxyvvXgRkUMIKQDMbBpwHxAF/M05d3ej6XHAP4DjgV3Axc65TcFpvwZ+AtQB1znn8pr7vLL9tRRsK2PF1jKWbw0052zYua9+eu/ucYzMSObckccwMrix7909Tht7EZEWaDYAzCwKeAg4CygEFprZXOfcygaz/QTY7ZwbZGYzgT8AF5vZMGAmMBzoC8w3s8HOubpDfd6aogpG3/ZO/fuMlASG9+3OeWMzGJGRzPCM7vTqFt+Kb1VERBoK5QhgArDOObcBwMzmADOAhgEwA/hdcPhF4EEL7I7PAOY456qBjWa2Lri+zw71YQmxUdw4dQgjM5IZ3rc7PbvGtfR7EhGREIQSABnAlgbvC4ETDjWPc85nZmVAz+D4zxstm3G4D8tKTeTq0weFUJaIiByJUB5J2VTDeuMuqw41TyjLYmZXmFm+meWXlJSEUJKIiBypUAKgEOjX4H0msO1Q85hZNJAMlIa4LM65R51zuc653PT09NCrFxGRVgslABYCOWbW38xiCZzUndtonrnArODwhcD7zjkXHD/TzOLMrD+QAyxom9JFRORINHsOINimfw2QR+Ay0MeccwVmdjuQ75ybC/wdeCp4kreUQEgQnO95AieMfcDVh7sCSERE2o85960meU/l5ua6/Px8r8sQEelQzGyRcy63JcuoX0IRkQilABARiVAKABGRCBV25wDMrAJY43UdIUgDdnpdRAhUZ9tSnW2rI9TZEWoEGOKca1HvVOH4NNA1LT2R4QUzy1d+gcRiAAAEYUlEQVSdbUd1ti3V2XY6Qo0QqLOly6gJSEQkQikAREQiVDgGwKNeFxAi1dm2VGfbUp1tpyPUCK2oM+xOAouISPsIxyMAERFpB2EVAGY2zczWmNk6M5vtdT1NMbN+ZvaBma0yswIzu97rmg7FzKLMbImZveF1LYdjZilm9qKZrQ7+XE/yuqbGzOyG4O97hZk9a2Zh0y2dmT1mZjvMbEWDcalm9q6ZrQ2+9gjDGv8Y/J0vM7NXzCzFyxqDNX2rzgbT/t3MnJmleVFbo1qarNPMrg1uQwvM7J7m1hM2AdCg68mzgWHAJcEuJcOND/iVc24ocCJwdZjWCXA9sMrrIkJwH/C2c+44YDRhVrOZZQDXAbnOuREEHoo409uqDvIEMK3RuNnAe865HOC94HsvPcG3a3wXGOGcGwV8Bfy6vYtqwhN8u07MrB+BbnE3t3dBh/AEjeo0s9MJ9MI4yjk3HPhTcysJmwCgQdeTzrka4EDXk2HFObfdObc4OFxBYGN12F7OvGBmmcC5wN+8ruVwzKw7MJHAE2VxztU45/Z4W1WTooGEYH8XiTTRr4VXnHMfE3gKb0MzgCeDw08C32/Xohppqkbn3DvOOV/w7ecE+gvx1CF+lgB/Bv6DJjq08sIh6rwKuDvYBS/OuR3NrSecAqCprifDbsPakJllA2OBL7ytpEn3EviD9XtdSDMGACXA48Hmqr+ZWZLXRTXknNtKYG9qM7AdKHPOveNtVc3q7ZzbDoGdFqCXx/U058fAPK+LaIqZTQe2Oue+9LqWZgwGTjWzL8zsIzMb39wC4RQAIXUfGS7MrCvwEvAL51y51/U0ZGbfBXY45xZ5XUsIooFxwP8458YC+/C+ueIgwfbzGUB/oC+QZGb/6m1VnYeZ3UygafVpr2tpzMwSgZuBW7yuJQTRQA8CTdM3As+bWVPb1XrhFAAhdR8ZDswshsDG/2nn3Mte19OEU4DpZraJQFPaGWb2v96WdEiFQKFz7sBR1IsEAiGcTAY2OudKnHO1wMvAyR7X1JxiMzsGIPjabHOAF8xsFvBd4AcuPK9JH0gg+L8M/j9lAovNrI+nVTWtEHjZBSwgcPR/2BPW4RQAoXQ96blgov4dWOWc+2+v62mKc+7XzrlM51w2gZ/j+865sNxjdc4VAVvMbEhw1JkEepALJ5uBE80sMfj7P5MwO1HdhIbdtM4CXvOwliaZ2TTgJmC6c67S63qa4pxb7pzr5ZzLDv4/FQLjgn+34eZV4AwAMxsMxNLMQ+zCJgCCJ4MOdD25CnjeOVfgbVVNOgX4IYG96qXBr3O8LqqDuxZ42syWAWOAuzyu5yDBo5MXgcXAcgL/N2Fzd6iZPQt8Bgwxs0Iz+wlwN3CWma0lcPXK3WFY44NAN+Dd4P/Rw17WCIesM+wcos7HgAHBS0PnALOaO6rSncAiIhEqbI4ARESkfSkAREQilAJARCRCKQBERCKUAkBEJEIpAEREIpQCQEQkQikAREQi1P8HBqJYjC6WAfwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"=> 使用XGBoost建模...\")\n",
    "XGB_native(train,test,features,features_non_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "position": {
    "height": "522.205px",
    "left": "1763.33px",
    "right": "20px",
    "top": "120px",
    "width": "350px"
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
